Non-invasive estimation of arterial blood gases

ABSTRACT

A non-invasive system and procedure for deriving the blood gas content for a patient. The system measures the carbon dioxide concentration of the expiratory breath relative to volume. This data is then processed to derive arterial blood gas levels of carbon dioxide. If data sampling is in the time domain, the processing shifts the data from the time domain to the volume domain. The processing also iteratively assesses the significance of numerous variables. The resulting relationship provides a fast and accurate measure of blood gas content for both healthy and diseased lung patients.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 08/592,726, filed Jan. 26, 1996, now U.S. Pat. No. 5,800,361 which is a continuation-in-part of Ser. No. 08/384,519 filed Feb. 6, 1995, U.S. Pat. No. 5,632,281, issued May 27, 1997.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods and apparatus for assessing the ventilatory status of a patient. More particularly, the present invention provides a system for implementing a non-invasive procedure for estimating the amount or concentration of dissolved carbon dioxide within the arterial portion of the vasculature. The arterial carbon dioxide content, expressed as a partial pressure, i.e., pCO₂, is an important measure of ventilatory status which ultimately reflects pulmonary health.

2. State of the Art

Physicians and other health care providers often use elevated arterial pCO₂ (PaCO₂) as an indicator of incipient respiratory failure. In this regard, the determination of PaCO₂ is useful in optimizing the settings on ventilators and detecting life-threatening blood gas changes in an anesthetized patient undergoing surgery. The traditional method for obtaining arterial blood gas values is to extract a sample of arterial blood and measure the partial pressure of carbon dioxide using a blood gas analyzer (PaCO₂ ^(ABG)). Arterial puncture has inherent limitations: 1) arterial puncture carries a degree of patient discomfort and risk, 2) handling of the blood is a potential health hazard to health care providers, 3) significant delays are often encountered before results are obtained and, 4) measurements can only be made intermittently.

Continuous invasive monitoring requires in-dwelling arterial lines which entail inherent problems. These include sepsis, slow response times, and signal decay. The nature of this monitoring system excludes its use under routine care and is generally restricted to intensive care units within a hospital facility.

There have been attempts to assess PaCO₂ levels indirectly, including a technique known as capnography. The approach utilized in capnography involves tracking patient exhalation and measuring expiratory gas CO₂ concentration against time during one or more respiratory cycles. The resulting relationship is plotted to create a graph depicting three distinct phases in breath CO₂ gas concentration during the patient's exhale cycle. (See, FIG. 1). Typically, the three phases reflect the clearing of the conducting airways which do not normally participate in gas exchange (i.e., airway dead space) (Phase I) followed by the exhalation of air from conducting airways dynamically mixed with lung gases from the active (alveoli) membrane surfaces within the lung that have undergone gas exchange with arterial blood (Phase II). The final phase (Phase III) reflects the exhalation of unmixed gas from regions of the lung which are normally in active exchange with the alveoli tissue. Phase III thus closely resembles (in healthy patients) gas properties associated with arterial blood in contact with the lung for gas exchange (CO₂ release and O₂ absorption). In normal lungs, the graph line of Phase III is substantially level (slope=0) since ventilated and perfused alveolar regions are closely matched. In a diseased lung, the Phase III line may not appear level due to a mismatch in ventilation and perfusion of this lung region. See, Table I below:

TABLE I Phase I Represents CO₂-free gas expired ftom the airway conduction structures where gas exchange does not occur, Phase II The S-shaped upswing in CO₂ concentration (expressed as a percent) represents the transition from airway to alveolar gas, and Phase III The alveolar plateau representing CO₂ rich gas from the alveoli.

In the past, capnography has utilized the peak or end-tidal (PetCO₂) values as an estimate of PaCO₂. PetCO₂ is a measure of the mean alveolar partial pressure of carbon dioxide from all functional gas exchange units of the lung. PetCO₂ obtained from capnography is a measure of mean alveolar pCO₂, which value approximates PaCO₂ in normal lungs. Because CO₂ readily diffuses across the alveolar-capillary membrane, the PetCO₂ closely approximates the PaCO₂ with normal ventilation-perfusion. The difference between PetCO₂ and PaCo₂ is primarily a function of the proportion of the lung where gas exchange does not occur (Fletcher, R., Johnson, G., and Brew, J., The Concept of Deadspace with Special Reference to Single Breath Test for Carbon Dioxide. Br. J. Anaesth., 53, 77, 1981). In patients afflicted with a lung disease, there often exists a proportional increase in the region of the lungs where gas exchange does not occur. This increase in so-called “alveolar dead space” results in a significant difference between peak CO₂ (PetCO₂) obtained from capnography and elevated arterial CO₂ (PaCO₂).

Other techniques have been utilized for assessing patient blood gas levels with mixed results. Transcutaneous sensors measure tissue pCO₂ diffused through the heated skin but have practical and theoretical limitations. Oximetry is a widely used, non-invasive method for estimating the arterial oxygen carried on hemoglobin. For example, U.S. Pat. Nos. 4,759,369, 4,869,254 and 5,190,038 describe pulse oximeters which measure the percentage of hemoglobin which is oxygenated. However, neither of the aforementioned techniques measures the amount of dissolved oxygen present, nor the amount of oxygen carried when hemoglobin levels are reduced. Low hemoglobin levels are found when there is a significant blood loss or when there is insufficient red blood cell formation. Additionally, oximeter readings are specific to the point of attachment, which is typically the finger tip or ear lobe, and may not reflect the oxygen level of vital organs during conditions such as shock or hypothermia.

There remains a significant need in the art for an accurate, non-invasive, sensitive method for accurately determining the levels of arterial blood gases. As will be seen hereinafter, the instant invention sets forth a non-invasive system to overcome the problems of the prior art.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a system to rapidly and accurately derive a patient's arterial carbon dioxide concentration and employs a non-invasive method for monitoring arterial partial pressure of carbon dioxide in a patient as an indicator of ventilatory status.

The present invention also comprises a system for detecting expiratory CO₂ concentration and volumetric rate data and accurately deriving actual arterial pCO₂ based thereon. The system affords non-invasive, substantially real time determination of blood gas concentrations as derived from current expiratory data as correlated with processed data collected from past expiratory measurements.

The present invention converts expiratory data from a time domain to a volume domain or accumulates data in the volume domain in the first instance. The arterial CO₂ partial pressures of a patient can then be ascertained by selectively analyzing the slope and intercept values associated with Phase II and Phase III expiratory data in the volume domain. If time domain data is converted to the volume domain, the conversion may be said to “normalize” the data by placing it in the volume domain, wherein time-dependent respiratory differences between patients are eliminated and a standardized gas concentration-to-incremental breath volume relationship is achieved.

The present invention also provides for the accurate determination of arterial CO₂ partial pressures by measuring expiratory gas data and statistically filtering this data to ascertain readings having the highest correlation to actual pulmonary performance with a statistically significant level of confidence.

The above and other advantages of the present invention are realized in a specifically delineated gas analysis and data processing system operated in accordance with select data qualifying and enhancing procedures. In particular, the inventive system provides for the collection of concise expiratory data from a patient undergoing treatment. This data includes details on CO₂ gas partial pressure, concentration, and total gas volume sampled during a breath exhaust cycle. Multiple readings are made to enhance accuracy. The expiratory data is converted to a volume domain from the time domain. The expiratory data is then charted to establish the three aforementioned distinct phases within the expiratory cycle. The associated linear details of these three phases are extracted and used to project current arterial CO₂ partial pressure, PaCO₂. This value is then utilized to quantify pulmonary performance and/or determine the existence of lung failure or distress.

In accordance with the varying aspects of the present invention, the system includes a sophisticated artificial intelligence engine that iteratively analyzes many separate and distinct measurements and the calculated values associated with the expiratory data. Based on these permutations, the engine quantifies those measured and derived values having the highest correlation to the actual arterial CO₂ partial pressures, resulting in a fixed relationship including specifically weighted variables for projecting arterial CO₂ partial pressures. This relationship, expressed as a vector, is implemented according to system parameters in actual patient monitoring during surgical procedures and other periods of time associated with potential pulmonary failure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing features of the present invention are more fully and readily understood from the following detailed description of a specific illustrative embodiment thereof, presented hereinbelow in conjunction with the accompanying drawings of which:

FIG. 1 graphically illustrates three phases of the CO₂volume curve in a healthy person;

FIG. 2 represents the normal expiration of CO₂ plotted against the expired volume and the represented dead spaces;

FIG. 3 is a schematic illustration of a system in accordance with a preferred embodiment of the present invention;

FIG. 4 is a flow chart of a preferred method of the present invention;

FIG. 5 is a capnograph for a healthy individual; and

FIG. 6 is a capnograph of a person suffering from lung distress.

DETAILED DESCRIPTION OF THE INVENTION

In overview, the present invention comprises a non-invasive system and procedure for deriving the gas content of arterial blood through analysis of the expiratory gas concentrations and volumes. The expiratory gas concentrations and volumes may be collected in a time domain, typically using a capnometer and pneumotachometer. Alternatively, the data may be collected directly in the volume domain with a time delay or phase-shift correction between the pneumotachometer and capnometer data. The raw data for both pCO₂ and volume are digitized at a frequency high enough to avoid aliasing. In adults with normal ventilatory frequencies, the data is typically collected at 100 Hz, producing a data point every 0.01 seconds. If taken in the time domain, the measurements are converted from the time domain (typically 1 point per 0.01 seconds) to a volume domain (typically 1 point per 1 ml of expired volume) utilizing a polynomial fit.

In the volume domain, the slope of the Phase II curve segment is calculated. The initial point of Phase II is where the curve transcends a threshold value (typically 0.5% for adults). The final point of Phase II is defined, in this application, as the point at which the curve deviates from linearity by a specified amount (typically 5% for adults). The threshold and deviation values are based on lung size and respiration rate. The slope of the Phase III segment is calculated, in an analogous manner, by calculating from the last data point of expiration toward Phase II until the curve deviates from linearity by a fixed amount (typically 5% for adults). The slopes of the Phase II and Phase III curve segments are used as initial input variables, which variables are combined into a vector with other parameters to produce an estimate of content of the gas in the arterial blood, i.e., the partial pressure of CO₂ or PaCO₂.

The following abbreviations and definitions are salient to invention description:

ABBREVIATIONS AND DEFINITIONS CO₂ Carbon dioxide pCO₂ Partial pressure of carbon dioxide PetCO₂ End tidal CO₂ PECO₂ Expiratory CO₂ PaCO₂ Arterial partial pressure of CO₂ PaCO₂ ^(ABG) Arterial partial pressure of CO₂ measured by an arterial blood gas analyzer % CO₂ Carbon dioxide value expressed as a percentage of the total gas content COPD Chronic obstruction pulmonary disease ml Milliliters fds Fowler dead space (Airway dead space) I2 Intercept of Phase II on CO₂ axis I3 Intercept of Phase III on CO₂ axis m2 Slope of Phase II m3 Slope of Phase III ph2i Phase II deviation index ph3i Phase III deviation index Tb Phase transition bend Tbi Phase transition bend index TV Tidal volume of air exhaled ang Angle between Phases II and III

As noted above, the expired CO₂ pattern obtained from capnography may be graphically depicted as a three-phase curve. In a normal person (FIG. 5), the CO₂ curve forms a plateau at the start of Phase III and reaches a value (PetCO₂) approximately equal to the PaCO₂ at the end of Phase III. In a patient with increased volume of the lung where gas exchange does not occur (physiologic or alveolar dead space, FIG. 6), as typically occurs in COPD, the curve does not plateau and the final PetCO₂ values are a less reliable indication of the actual PaCO₂. The instant invention determines an estimated value which closely approximates the actual PaCO₂ under these and other conditions.

The inventive system is illustrated in FIG. 3 and comprises a differential pressure flowmeter or pneumotachometer (item 15), a flow signal unit (item 14), a gas sensor (item 16), a CO₂ signal unit (item 13), a processor (item 12) and a data display (item 11). This system can be used with or without mechanical ventilation of the patient.

Many devices for measuring the volume of a person's expiratory breath already possess the capability to integrate a measured flow and can be used in this invention. Typically, flow-measuring devices use one of the following methods to determine flow:

1. measurement of pressure drop or differential pressure across a fixed resistance (differential pressure flowmeter or pneumotachometer),

2. measurement of the temperature change of a heated wire cooled by the airflow (hot wire anemometer),

3. measurement of frequency shift of an ultrasonic beam passed through the airstream (ultrasonic Doppler),

4. counting the number of vortices shed as air flows past a strut (vortex shedding), or

5. measurement of transmission time of a sound or heat impulse created upstream to a downstream sensor (time of flight device).

Alternatively, volume may be measured directly by counting revolutions of a vane placed in the flow path (spinning vane). A discussion of the aforementioned devices and associated technology can be found in Sullivan, et al., Pneumotachographs: Theory and Clinical Application, Respiratory Care, Vol. 29-7, pages 736-749 (1984), which is incorporated by reference herein. Examples of known differential pressure flowmeters include those described in U.S. Pat. Nos. 4,047,521, 4,403,514, 5,038,773, 5,088,332, 5,347,843 and 5,379,650, the teachings of which are incorporated by reference herein.

The exemplary device for respiratory flow measurement is the differential pressure flowmeter or “pneumotachometer” (FIG. 3, item 15), which provides a pressure differential indicative of respiratory flow, the differential being converted via transducers in flow signal unit (item 14) to electrical signals representative of the relationship between respiratory flow and pressure differential. The flowmeter (item 15) is manufactured and sold by Novametrix Medical Systems, Inc., Wallingford, Conn. However, any of the aforementioned types of flow measurement devices may be utilized in the inventive system.

Sensors capable of measuring carbon dioxide content in a person's expiratory breath are well known. The currently preferred device for measuring carbon dioxide content is a gas analyzer of the type employing non-dispersive infrared radiation which presents data representing the % CO₂ (or pCO₂) of a sample of exhaled breath. Examples of known infrared gas analyzers include those described in U.S. Pat. Nos. 4,859,858, 4,859,859, 4,914,720, 4,958,075, 5,146,092, 5,153,436, 5,206,511 and 5,251,121, the teachings of which patents are incorporated by reference herein. Other technologies used to measure the concentration of carbon dioxide such as Raman spectroscopy and mass spectroscopy can also be used in the present invention.

The exemplary gas sensor (FIG. 3, item 16) capable of measuring carbon dioxide content in a patient's exhaled breath is available from Novametrix Medical Systems, Inc., Wallingford, Conn., under the trade name CAPNOSTAT™. Other methods of measuring carbon dioxide content both at the airway (mainstream) or by removing a sample (sidestream) may be used in the present invention.

Gas analyzers, as described above, employ non-dispersive infrared radiation to measure the concentration of a selected gas in a mixture of gases. The infrared radiation can be emitted from a thick film source and focused by a mirror to pass through the mixture of gases being analyzed. After passing through the gas mixture, the infrared beam is passed through a filter which reflects all of the radiation except for those in the narrow bands centered on a wavelength which is absorbed by the gas of concern (such as CO₂) in the mixture being analyzed (such as the air flow from a person's expired breath). This narrow band of radiation, which typically extends approximately 190 angstroms to each side of the wavelength on which the radiation is centered, is allowed to reach a detector which is capable of producing an electrical output signal inversely proportional to the magnitude of the infrared radiation impinging upon it, as the radiation in that band is attenuated to an extent which is proportional to the concentration of the designated gas in the mixture of gases being analyzed. The strength of the signal generated by the detector is consequently inversely proportional to the concentration of the designated gas and can be inverted to provide a signal indicative of that concentration. The processor can be either a personal computer with a suitable digital interface for receiving the digital signals from the CO₂ signal unit (FIG. 3, item 13) and flow signal unit (FIG. 3, item 14), or any specially designed processor capable of calculating the vectors as disclosed further herein.

As discussed above, capnography systems in the prior art have attempted to estimate the PaCO₂ by using the peak or end-tidal value for a single breath (PetCO₂). Such systems are effective at tracking gas changes in normal lungs but have been shown to be unreliable in diseased lungs, such as in COPD, or when a significant pulmonary shunt exists. The inadequacy of PetCO₂ to measured PaCO₂ is, in part, attributed to regions within the lung with high ventilation to perfusion ratios. Exhaled gases from regions where gas exchange does not occur due to inadequate perfusion reduce the obtained PetCO₂. For example, in the diseased lung, the increased slope of Phase III due to impaired gas exchange makes the PetCO₂ value a less reliable indicator of PaCO₂.

In the instant invention, the digitized expired CO₂ and flow data, if taken in the time domain, is converted to the volume domain to account for variations between different people and thereby improve accuracy. Alternatively, flow signal unit 14 may be employed to integrate a flow rate signal on a continuing basis into volume, CO₂ readings then being taken at predetermined volumetric intervals, a correction being made for the phase shift between the two sensors (including signal processing time) as data is taken or subsequently processed. Alternatively, all data may be taken in the time domain and converted to the volume domain, after the fact, by a processing unit (FIG. 3, item 12). Finally, as previously noted, flow volume may be measured directly, as by a spinning vane device. CO₂ concentrations versus respiratory flow or volume may be depicted as a curve including a series of units, each defined by a pair of points. In the time domain, the progression from unit to unit is based on fractions of a second, regardless of the quantity of air expelled during that time period. The effective rate of sampling depends on the patient's rate of respiration. In the volume domain, however, each unit to unit movement is based on a unit of volume of air expelled, regardless of the expiratory time. By employing a volume domain or by transferring the data from a time domain to a volume domain, a more physiologic relationship is expressed. Toward the end of the breath, relative percentage increments in expiratory time and volume vary greatly in magnitude, particularly when comparing healthy individuals to patients with impaired respiratory function. By using the volume domain, the latter portion of the breath receives a weighting more indicative of its physiologic importance, regardless of the health of the patient. For example, in a mechanically ventilated, chemically paralyzed patient with COPD experiencing bronchospasm, the exhalation period is prolonged due to the narrowing of the airways (FIG. 6). With pharmaceutical intervention, expiratory resistance can be reduced, thereby reducing time required to complete exhalation. The exhalation time greatly varies between the non-medicated and medicated patient, yet the exhaled CO₂ to total gas volume exhaled ratio is relatively constant. Plotted in the volume domain, the capnograph of non-medicated and medicated patients would be the same. Conversely, in the prior art systems (time domain), measurements taken before and after medication would vary greatly.

As shown in FIG. 3, as a person exhales, the CO₂ sensor (item 16) measures the pCO₂ in the person's expiratory breath. Virtually simultaneously, the pneumotachometer (item 15) measures the flow of the person's expiratory breath using differential pressures across a fixed resistance as previously referenced. The differential pressure values from the pneumotachometer (item 15) are converted to electrical signals and digitized in flow signal unit (item 14) and the analog signals from CO₂ sensor (item 16) are digitized in CO₂ signal unit (item 13). The digital signals are processed, as further disclosed herein, in the processor (item 12) and displayed via printer, VDT, LED or other display devices as known in the art (item 11). The estimated PaCO₂ value is displayed after several breaths. The actual lag time is dependent on the consistency of the data with a minimum of six breaths initially and every third breath thereafter. This data flow is illustrated, as part of the complete conversion routine, in FIG. 4. As stated heretofore, the CO₂ sensor (item 16) and associated signal unit (item 13) measures the CO₂ content of the patient's breath. The output from the pneumotachometer (item 15) is representative of the flow of the expiratory breath. The pneumotachometer (item 15) outputs are converted via transducers in flow signal unit (item 14) to electrical signals which are digitized as time dependent signals representing the flow rate of the patient's breath, and subsequently integrated into volume. These signals are sent to the processor (item 12).

As shown in the flow chart in FIG. 4, the flow data and CO₂ data are isolated breath-by-breath and converted to the volume domain in order to express a more physiologic relationship. The volume domain progression from unit to unit is based on a unit of volume expired without regard to time. As a preliminary programming step, a variety of variables is computed which depict features of Phase II, the transition period, and Phase III. The computed variables are selected to include all features and idiosyncrasies of the capnograph. The derived variables are of two types: 1) those with established physiologic importance, and 2) those which mathematically represent features of the capnograph whose physiologic significance is less clear. These derived variables have utility in normalization of the data to allow for diverse lung sizes and respiratory patterns. Artificial intelligence is then used to evaluate each variable and assign a mathematical weight. In particular, a neural network is employed to insure the variables are evaluated without imposing an initial bias. These variables are computed as described hereafter.

Suitable software systems are readily available in the marketplace and are exemplified by the neural network presently offered by NeuralWare, Inc. located at Penn Center West, Building IV, Suite 227, Pittsburgh, Pa. 15276.

Computation of Intermediate Variables

A threshold level of CO₂ is detected by finding the point where the curve transcends from a value below 0.5% to a value above 0.5%. In the instant invention, this point is used as the initial point of Phase II. From the threshold point, subsequent CO₂ data points are adjoined and tested for linearity to the data point where the signal deviates from a linear path. The amount of allowed deviation is typically 5% in adults. The point where the deviation occurs marks the termination of Phase II and the slope of the Phase II segment is derived. As described further herein, the Phase II slope is used later. The slope of Phase III is determined in an analogous manner by starting at the last data point of expiration (PetCO₂) and regressing toward the termination point of Phase II. Once the Phase II and III slopes are computed, the remaining input variables are derived as the following:

TABLE II Fds The Fowler dead space volume is determined by examining the volume that gives equal area between the start of the Phase II data and the start of the Phase III data. This computation is done by fractional difference. (Fowler W.S. Lung Function Studies II. The Respiratory Dead Space. Am. J. Physiol. 154: 405, 1948 - the teachings of which are hereby incorporated by reference). i2 The CO₂ axis intercept of Phase II. The effective Phase II slope (m0) is used at the Fds volume. The equation is: i2 = modelCO₂ (Fds) − m0 * Fds where modelCO₂ (Fds) is the model evaluated at Fds. m3 The slope of Phase III. i3 The CO₂ axis intercept of Phase III. The Phase III slope is used at the end tidal volume. The equation is: i2 = modelCO₂ (PetCO₂) − m3 * PetCO₂ where modelCO₂ (PetCO₂) is the model evaluated at PetCO₂. ang The angle in degrees between Phase II and Phase III. The effective slope of Phase II (m0) is used. The angle between the lines is: (tan(m3) − tan(m0) + π) * (180/π). PetCO₂ The end tidal value of pCO₂. The last element in the CO₂ (vol) data array is used for PetCO₂. TV The total volume of air exhaled expressed in ml. ph2i The Phase II deviation index. This is the smallest volume at which the Phase II line deviates from the smoothed CO₂ data. The comparison is done by fractional difference. ph3i The Phase III deviation index. This is the smallest volume at which the Phase III line deviates from the smoothed CO₂ data by the phase transition width (Tw). The negative of the second derivative of the model curve exhibits a peak in the region between Phase II and Phase III. The width of this peak is Tw and is determined as the full width at half the maximum of the peak. All second derivative curves have peaks that fall below half the maximum of increasing volume. However, the peaks in some derivative curves do not fall to ½ the maximum for decreasing volume. Thus, Tw is found from the right side half-width of the peak at half-maximum and multiplying by 2 to construct the full-width. Tb The phase transition bend. This value is the maximum of the negative of the second derivative of the model curve. It reflects the measure of the sharpness of the transition. Tbi The phase transition bend index. This value is the index (volume) at which the phase transition bend (Tb) occurs. ModAng A modified version of the angle in degrees between the Phase II and Phase III lines. The modification scales the actual angle to enhance differences in the model fit curves. The scaling is heuristic. The modified angle is computed as: (tan(400 * m3) − tan(400 * m0) + π)(180/π).

The product of these intermediate variables and their assigned mathematical weights is used in arguments in a hyperbolic scaling function to compute the final PaCO₂. The mathematical weights for each variable were initially defined from a set of 100 derived variables used as inputs to a fully connected, back-propagation, neural network and the actual arterial carbon dioxide used as the desired output. Once trained for 75,000 iterations, the resultant weights were extracted and used for subsequent derivations where the actual PaCO₂ was to be determined.

System operation, based on the stored vector arguments, is shown in the flow chart in FIG. 4. As described herein, the system inputs the flow data and CO₂ data as isolated breath-by-breath and converted to the volume domain in order to express a more physiologic relationship. The volume domain progression from unit to unit is based on a unit of volume expired without regard to time. Referring to FIG. 4, logic begins at start block 100, and begins a processing loop defined by block 110, defining a sequence of 10 iterations (exemplary). At block 120, the digitized respiratory data is loaded as variable RESP(I), converted to the volume domain at block 130, and then mapped, setting forth the three aforementioned phases of the capnograph, block 140, VRESP(I).

At block 150, Phase II threshold is detected, THRESH(I), followed by block 160 wherein the detection of the Phase II deviation to Phase III, DEVIA(I), is effected. Based thereon, the variables for the capnograph are determined, VAR(I), at block 170. The stored arguments based on the assessed weighting for the vector are recalled at block 180, and the final vector generated, VEC(I). Based thereon, the PaCO₂ is derived and stored for the Ith iteration, block 190. At block 200, this process is repeated for the next sequence of respiratory data from the patient, for 10 iterations or breath cycles (exemplary).

Starting at block 210, the data is refined by discarding four of the ten readings (two high and two low), XPaCO₂(I), and the resulting six iteratives are statistically assessed and accepted if meeting a minimum confidential level (95 or 99%), by way of example. If accepted, the mean value of the remaining iterations is displayed, MPaCO₂(I), block 220. The process continues giving pseudo real time data, as noted at block 230.

The results of system operation are depicted for the following patients:

TABLE III Derived PaCO₂ PaCO₂ Patient No. PaCO₂ ^(ABG) mean std err  1. 33.00 34.30 2.40 1.30  2. 33.00 31.33 1.84 1.67  3. 33.00 32.20 2.33 0.80  4. 33.50 32.00 1.77 1.50  5. 38.30 39.67 1.36 1.39  6. 40.00 38.37 0.86 1.63  7. 40.00 41.38 0.62 1.38  8. 42.00 42.63 0.15 0.63  9. 42.00 44.01 1.60 2.01 10. 44.00 45.47 0.18 1.47 11. 45.00 45.66 2.35 0.66 12. 46.00 46.40 1.31 0.40 13. 47.00 47.42 0.96 0.42 14. 49.00 51.03 0.12 2.03 15. 50.50 50.73 0.50 0.23 16. 51.00 49.77 0.31 1.23 17. 51.00 52.07 1.29 1.07 18. 51.00 52.22 0.69 1.22 19. 53.00 53.20 0.72 0.20 20. 57.00 56.60 1.22 1.00 21. 63.00 63.34 0.23 0.34 22. 65.00 62.20 0.77 2.80 23. 67.00 64.98 0.62 2.02

The exemplary data was computed for each breath contained in a 2-5 minute collection period. The calculated PaCO₂ values and standard deviations included in Table III were determined by analysis of the median six (6) values often (10) consecutive breaths. The standard deviations for the six (6) median values for each patient were computed for assurance of reproducibility of the data. The computed values were then compared to the PaCO₂ ^(ABG) values obtained simultaneously from an arterial blood gas sample. Using this method, the accuracy for determination of PaCO₂ by the method of the instant invention is typically about ±2 mmHg. However, rather than outputting a single PeCO₂ value, the actual data range for 95 or 99% confidence levels may be graphically displayed, as on the data display (FIG. 3, item 11).

Since other modifications and changes, varied to fit particular operating requirements and environments, will be apparent to those skilled in the art, the invention is not considered limited to the example chosen for the purposes of disclosure, and covers all changes and modifications which do not constitute departures from the true spirit and scope of this invention. 

What is claimed is:
 1. A non-invasive arterial gas partial pressure determination system, comprising: expiratory gas sampling means for taking one or more samples of expiratory gases from a patient and developing a gas profile for at least one constituent of said sampled gas; and gas sampling data processing means for receiving data input representative of said gas profile and selectively processing said inputted data to determine at least one arterial gas partial pressure value.
 2. A method for noninvasively determining a content of a Gas in blood of a patient, comprising: obtaining data representative of the gas for at least one breath; and evaluating a trend of said data over at least a portion of said at least one breath to determine the content of the gas.
 3. The method of claim 2, wherein said obtaining data comprises obtaining data representative of carbon dioxide.
 4. The method of claim 3, wherein said obtaining data representative of carbon dioxide comprises obtaining data representative of carbon dioxide expelled by alveoli.
 5. The method of claim 2, wherein said obtaining data comprises taking time domain measurements.
 6. The method of claim 5, wherein said taking time domain measurements is effected at a frequency of about 10 Hz to about 1,000 Hz.
 7. The method of claim 5, further comprising converting said time domain measurements to a volume domain.
 8. The method of claim 4, wherein said obtaining data comprises taking time domain measurements during at least one expiratory breath of the patient.
 9. The method of claim 8, wherein said obtaining data further comprises calculating the content of carbon dioxide in the blood of the patient.
 10. The method of claim 8, wherein said taking time domain measurements is effected at a frequency of about 10 Hz to about 1,000 Hz.
 11. The method of claim 8, further comprising converting said time domain measurements to a volume domain.
 12. The method of claim 2, wherein said taking time domain measurements is effected with a pneumotachometer and a gas sensor responsive to a presence of the gas.
 13. The method of claim 4, wherein said evaluating said trend comprises: mapping said data in a volume domain to form a curve; determining a first data point at which said curve transcends a threshold carbon dioxide content; calculating a first slope of said data from said first data point to a second data point at which said data deviates from a substantially linear path; calculating, a second slope of said data from said second data point to a final expiratory data point of said data; extracting said first slope and said second slope to create an expiratory relationship; combining said expiratory relationship with stored arguments into at least one vector; and using estimating the content of carbon dioxide based on said at least one vector.
 14. The method of claim 13, wherein said determining said first data point comprises determining a data point equal to about 0.5% of the content of carbon dioxide in the blood of the patient.
 15. The method of claim 13, further comprising determining a partial pressure of carbon dioxide in the blood of the patient based on the carbon dioxide content.
 16. The method of claim 2, further comprising filtering said data representative of the gas.
 17. A method for noninvasively determining a content of carbon dioxide in blood of a patients comprising: obtaining, data representative of carbon dioxide expelled by alveoli of the patient for at least one breath; and evaluating a trend of said data over said at least one breath to determine the said content of carbon dioxide.
 18. The method of claim 17, wherein said obtaining data comprises taking time domain measurements during at least one expiratory breath of the patient.
 19. The method of claim 18, wherein said obtaining data further comprises measuring the content of carbon dioxide in the blood of the patient.
 20. The method of claim 18, wherein said taking time domain measurements is effected at a frequency of about 10 Hz to about 1,000 Hz.
 21. The method of claim 18, wherein said taking time domain measurements is effected with a pneumotachometer and a carbon dioxide sensor.
 22. The method of claim 18, further comprising converting said time domain measurements to a volume domain.
 23. The method of claim 17, wherein said evaluating said trend comprises: mapping said data in a volume domain to form a curve; determining a first data point at which said curve transcends a threshold carbon dioxide content; calculating a first slope of said data from said first data point to a second data point at which said data deviates from a substantially linear path; calculating a second slope of said data from said second data point to a final expiratory data point of said data; extracting said first slope and said second slope to create an expiratory relationship; combining said expiratory relationship with stored arguments into at least one vector; and using estimating the content of carbon dioxide based on said at least one vector.
 24. The method of claim 23, wherein said determining said first data point comprises determining a data point equal to about 0.5% of the content of carbon dioxide in the blood of the patient.
 25. The method of claim 23, further comprising determining a partial pressure of carbon dioxide in the blood of the patient based on the carbon dioxide content.
 26. The method of claim 17, further comprising filtering said data representative of carbon dioxide expelled by alveoli. 